Overview

Dataset statistics

Number of variables27
Number of observations5332
Missing cells996
Missing cells (%)0.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 MiB
Average record size in memory216.0 B

Variable types

Numeric17
Categorical9
Boolean1

Alerts

ct_ali_liquida has constant value ""Constant
EntradaInicial has a high cardinality: 1131 distinct valuesHigh cardinality
EntradaFinal has a high cardinality: 1198 distinct valuesHigh cardinality
na_nombre has a high cardinality: 599 distinct valuesHigh cardinality
na_rega has a high cardinality: 469 distinct valuesHigh cardinality
gr_direccion has a high cardinality: 354 distinct valuesHigh cardinality
gr_poblacion has a high cardinality: 146 distinct valuesHigh cardinality
ct_codigo is highly overall correlated with ct_razaHigh correlation
ct_granja is highly overall correlated with ct_tipo and 1 other fieldsHigh correlation
ct_raza is highly overall correlated with ct_codigo and 2 other fieldsHigh correlation
ct_fase is highly overall correlated with ct_sexo and 1 other fieldsHigh correlation
ct_sexo is highly overall correlated with ct_fase and 3 other fieldsHigh correlation
IncPeso is highly overall correlated with DiasMedios and 3 other fieldsHigh correlation
DiasMedios is highly overall correlated with IncPeso and 3 other fieldsHigh correlation
GMD is highly overall correlated with DiasMedios and 2 other fieldsHigh correlation
NumAnimales is highly overall correlated with NumBajas and 2 other fieldsHigh correlation
PesoEntMedio is highly overall correlated with ct_tipo and 1 other fieldsHigh correlation
PesoRecMedio is highly overall correlated with IncPeso and 2 other fieldsHigh correlation
NumBajas is highly overall correlated with NumAnimalesHigh correlation
ct_tipo is highly overall correlated with ct_granja and 10 other fieldsHigh correlation
ct_tipo_ali is highly overall correlated with ct_granja and 10 other fieldsHigh correlation
se_nombre is highly overall correlated with ct_fase and 3 other fieldsHigh correlation
ct_tipo is highly imbalanced (53.6%)Imbalance
ct_tipo_ali is highly imbalanced (53.8%)Imbalance
gr_direccion has 979 (18.4%) missing valuesMissing
ct_codigo has unique valuesUnique
IncPeso has unique valuesUnique
GMD has unique valuesUnique
ct_raza has 2300 (43.1%) zerosZeros

Reproduction

Analysis started2023-02-07 08:18:48.296146
Analysis finished2023-02-08 23:57:25.247203
Duration1 day, 15 hours and 38 minutes
Software versionydata-profiling vv4.0.0
Download configurationconfig.json

Variables

ct_codigo
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct5332
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean181943.61
Minimum2522
Maximum205954
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.8 KiB
2023-02-09T01:04:23.654683image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2522
5-th percentile3116.55
Q1200899.75
median202499.5
Q3204150.25
95-th percentile205572.45
Maximum205954
Range203432
Interquartile range (IQR)3250.5

Descriptive statistics

Standard deviation59418.747
Coefficient of variation (CV)0.32657782
Kurtosis4.4636927
Mean181943.61
Median Absolute Deviation (MAD)1629
Skewness-2.5215429
Sum9.7012332 × 108
Variance3.5305875 × 109
MonotonicityNot monotonic
2023-02-09T01:04:23.766665image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20312 1
 
< 0.1%
200110 1
 
< 0.1%
203328 1
 
< 0.1%
201160 1
 
< 0.1%
202228 1
 
< 0.1%
202794 1
 
< 0.1%
203956 1
 
< 0.1%
202304 1
 
< 0.1%
3037 1
 
< 0.1%
204660 1
 
< 0.1%
Other values (5322) 5322
99.8%
ValueCountFrequency (%)
2522 1
< 0.1%
2523 1
< 0.1%
2525 1
< 0.1%
2526 1
< 0.1%
2570 1
< 0.1%
2573 1
< 0.1%
2601 1
< 0.1%
2609 1
< 0.1%
2653 1
< 0.1%
2660 1
< 0.1%
ValueCountFrequency (%)
205954 1
< 0.1%
205949 1
< 0.1%
205948 1
< 0.1%
205947 1
< 0.1%
205946 1
< 0.1%
205945 1
< 0.1%
205943 1
< 0.1%
205942 1
< 0.1%
205941 1
< 0.1%
205940 1
< 0.1%

ct_integra
Real number (ℝ)

Distinct368
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean353.49944
Minimum1
Maximum649
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.8 KiB
2023-02-09T01:04:23.889241image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q1230
median383
Q3492
95-th percentile590
Maximum649
Range648
Interquartile range (IQR)262

Descriptive statistics

Standard deviation172.70107
Coefficient of variation (CV)0.48854695
Kurtosis-0.72288869
Mean353.49944
Median Absolute Deviation (MAD)121
Skewness-0.52899478
Sum1884859
Variance29825.661
MonotonicityIncreasing
2023-02-09T01:04:24.132203image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 302
 
5.7%
504 199
 
3.7%
484 145
 
2.7%
418 84
 
1.6%
160 82
 
1.5%
369 72
 
1.4%
108 58
 
1.1%
271 53
 
1.0%
318 48
 
0.9%
94 47
 
0.9%
Other values (358) 4242
79.6%
ValueCountFrequency (%)
1 302
5.7%
10 22
 
0.4%
20 11
 
0.2%
22 1
 
< 0.1%
23 19
 
0.4%
29 5
 
0.1%
34 22
 
0.4%
36 12
 
0.2%
40 12
 
0.2%
43 11
 
0.2%
ValueCountFrequency (%)
649 1
< 0.1%
647 1
< 0.1%
646 1
< 0.1%
645 2
< 0.1%
644 1
< 0.1%
643 1
< 0.1%
642 1
< 0.1%
641 1
< 0.1%
640 2
< 0.1%
639 1
< 0.1%

ct_granja
Real number (ℝ)

Distinct31
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9208552
Minimum1
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.8 KiB
2023-02-09T01:04:24.233460image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile20
Maximum70
Range69
Interquartile range (IQR)1

Descriptive statistics

Standard deviation8.2141955
Coefficient of variation (CV)2.095001
Kurtosis20.060366
Mean3.9208552
Median Absolute Deviation (MAD)0
Skewness4.2164768
Sum20906
Variance67.473007
MonotonicityNot monotonic
2023-02-09T01:04:24.324711image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 3308
62.0%
2 777
 
14.6%
3 260
 
4.9%
4 151
 
2.8%
38 141
 
2.6%
5 138
 
2.6%
6 78
 
1.5%
20 69
 
1.3%
7 65
 
1.2%
9 48
 
0.9%
Other values (21) 297
 
5.6%
ValueCountFrequency (%)
1 3308
62.0%
2 777
 
14.6%
3 260
 
4.9%
4 151
 
2.8%
5 138
 
2.6%
6 78
 
1.5%
7 65
 
1.2%
8 42
 
0.8%
9 48
 
0.9%
10 19
 
0.4%
ValueCountFrequency (%)
70 2
 
< 0.1%
69 2
 
< 0.1%
68 1
 
< 0.1%
64 15
 
0.3%
60 3
 
0.1%
55 3
 
0.1%
46 3
 
0.1%
38 141
2.6%
31 7
 
0.1%
30 10
 
0.2%

ct_nave
Real number (ℝ)

Distinct54
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.990248
Minimum1
Maximum1201
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.8 KiB
2023-02-09T01:04:24.438151image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile10
Maximum1201
Range1200
Interquartile range (IQR)1

Descriptive statistics

Standard deviation89.870815
Coefficient of variation (CV)5.9952856
Kurtosis88.89641
Mean14.990248
Median Absolute Deviation (MAD)0
Skewness8.8311256
Sum79928
Variance8076.7634
MonotonicityNot monotonic
2023-02-09T01:04:24.548573image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 3843
72.1%
3 291
 
5.5%
4 241
 
4.5%
2 231
 
4.3%
5 187
 
3.5%
6 111
 
2.1%
8 73
 
1.4%
7 51
 
1.0%
10 50
 
0.9%
9 31
 
0.6%
Other values (44) 223
 
4.2%
ValueCountFrequency (%)
1 3843
72.1%
2 231
 
4.3%
3 291
 
5.5%
4 241
 
4.5%
5 187
 
3.5%
6 111
 
2.1%
7 51
 
1.0%
8 73
 
1.4%
9 31
 
0.6%
10 50
 
0.9%
ValueCountFrequency (%)
1201 6
0.1%
1101 6
0.1%
1001 6
0.1%
901 1
 
< 0.1%
801 1
 
< 0.1%
701 6
0.1%
605 7
0.1%
601 6
0.1%
505 12
0.2%
501 13
0.2%

ct_tipo
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size41.8 KiB
1
4808 
2
524 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5332
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
1 4808
90.2%
2 524
 
9.8%

Length

2023-02-09T01:04:24.655481image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-09T01:04:24.746259image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1 4808
90.2%
2 524
 
9.8%

Most occurring characters

ValueCountFrequency (%)
1 4808
90.2%
2 524
 
9.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5332
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4808
90.2%
2 524
 
9.8%

Most occurring scripts

ValueCountFrequency (%)
Common 5332
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4808
90.2%
2 524
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5332
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4808
90.2%
2 524
 
9.8%

ct_raza
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.717742
Minimum0
Maximum97
Zeros2300
Zeros (%)43.1%
Negative0
Negative (%)0.0%
Memory size41.8 KiB
2023-02-09T01:04:24.817115image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median15
Q380
95-th percentile85
Maximum97
Range97
Interquartile range (IQR)80

Descriptive statistics

Standard deviation37.715539
Coefficient of variation (CV)1.0559329
Kurtosis-1.7450501
Mean35.717742
Median Absolute Deviation (MAD)15
Skewness0.31525945
Sum190447
Variance1422.4619
MonotonicityNot monotonic
2023-02-09T01:04:24.908123image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 2300
43.1%
84 764
 
14.3%
69 507
 
9.5%
23 369
 
6.9%
80 357
 
6.7%
15 263
 
4.9%
7 157
 
2.9%
65 153
 
2.9%
93 128
 
2.4%
94 86
 
1.6%
Other values (12) 248
 
4.7%
ValueCountFrequency (%)
0 2300
43.1%
7 157
 
2.9%
15 263
 
4.9%
18 1
 
< 0.1%
23 369
 
6.9%
65 153
 
2.9%
66 54
 
1.0%
68 40
 
0.8%
69 507
 
9.5%
80 357
 
6.7%
ValueCountFrequency (%)
97 3
 
0.1%
96 3
 
0.1%
95 2
 
< 0.1%
94 86
 
1.6%
93 128
 
2.4%
90 2
 
< 0.1%
89 3
 
0.1%
88 21
 
0.4%
85 51
 
1.0%
84 764
14.3%

ct_fase
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.513128
Minimum2
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.8 KiB
2023-02-09T01:04:24.989377image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q12
median2
Q321
95-th percentile22
Maximum24
Range22
Interquartile range (IQR)19

Descriptive statistics

Standard deviation9.6790561
Coefficient of variation (CV)0.92066375
Kurtosis-1.926228
Mean10.513128
Median Absolute Deviation (MAD)0
Skewness0.26053617
Sum56056
Variance93.684127
MonotonicityNot monotonic
2023-02-09T01:04:25.074233image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 3004
56.3%
21 1154
 
21.6%
22 1103
 
20.7%
20 34
 
0.6%
23 20
 
0.4%
24 17
 
0.3%
ValueCountFrequency (%)
2 3004
56.3%
20 34
 
0.6%
21 1154
 
21.6%
22 1103
 
20.7%
23 20
 
0.4%
24 17
 
0.3%
ValueCountFrequency (%)
24 17
 
0.3%
23 20
 
0.4%
22 1103
 
20.7%
21 1154
 
21.6%
20 34
 
0.6%
2 3004
56.3%

ct_sexo
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6449737
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.8 KiB
2023-02-09T01:04:25.136913image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median4
Q35
95-th percentile8
Maximum8
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.8895072
Coefficient of variation (CV)0.51838705
Kurtosis0.11362732
Mean3.6449737
Median Absolute Deviation (MAD)2
Skewness0.9619965
Sum19435
Variance3.5702373
MonotonicityNot monotonic
2023-02-09T01:04:25.207687image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 2511
47.1%
4 1156
21.7%
5 1104
20.7%
8 524
 
9.8%
3 20
 
0.4%
1 17
 
0.3%
ValueCountFrequency (%)
1 17
 
0.3%
2 2511
47.1%
3 20
 
0.4%
4 1156
21.7%
5 1104
20.7%
8 524
 
9.8%
ValueCountFrequency (%)
8 524
 
9.8%
5 1104
20.7%
4 1156
21.7%
3 20
 
0.4%
2 2511
47.1%
1 17
 
0.3%
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
False
5332 
ValueCountFrequency (%)
False 5332
100.0%
2023-02-09T01:04:25.294091image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

ct_tipo_ali
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size41.8 KiB
1
4811 
2
521 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5332
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
1 4811
90.2%
2 521
 
9.8%

Length

2023-02-09T01:04:25.354616image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-09T01:04:25.432454image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1 4811
90.2%
2 521
 
9.8%

Most occurring characters

ValueCountFrequency (%)
1 4811
90.2%
2 521
 
9.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5332
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4811
90.2%
2 521
 
9.8%

Most occurring scripts

ValueCountFrequency (%)
Common 5332
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4811
90.2%
2 521
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5332
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4811
90.2%
2 521
 
9.8%

IncPeso
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct5332
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90.819295
Minimum2.0073623
Maximum153.66376
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.8 KiB
2023-02-09T01:04:25.531592image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.0073623
5-th percentile78.344903
Q186.083146
median90.875544
Q395.315632
95-th percentile108.3306
Maximum153.66376
Range151.6564
Interquartile range (IQR)9.2324862

Descriptive statistics

Standard deviation11.591864
Coefficient of variation (CV)0.12763658
Kurtosis11.504506
Mean90.819295
Median Absolute Deviation (MAD)4.6073154
Skewness-1.063554
Sum484248.48
Variance134.37131
MonotonicityNot monotonic
2023-02-09T01:04:25.643408image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.8633485 1
 
< 0.1%
84.37082756 1
 
< 0.1%
90.72997886 1
 
< 0.1%
87.49503376 1
 
< 0.1%
95.3432563 1
 
< 0.1%
91.2816265 1
 
< 0.1%
94.60636974 1
 
< 0.1%
82.81739157 1
 
< 0.1%
90.92723093 1
 
< 0.1%
89.57185818 1
 
< 0.1%
Other values (5322) 5322
99.8%
ValueCountFrequency (%)
2.007362254 1
< 0.1%
6 1
< 0.1%
8.902439024 1
< 0.1%
11 1
< 0.1%
12.29095074 1
< 0.1%
12.61040356 1
< 0.1%
12.83122313 1
< 0.1%
13.45948539 1
< 0.1%
15 1
< 0.1%
15.86401633 1
< 0.1%
ValueCountFrequency (%)
153.663761 1
< 0.1%
152.6285176 1
< 0.1%
146.7098446 1
< 0.1%
144.2729737 1
< 0.1%
141.4440678 1
< 0.1%
141.2597305 1
< 0.1%
140.5350069 1
< 0.1%
140.5148603 1
< 0.1%
140.3575542 1
< 0.1%
137.0921986 1
< 0.1%

DiasMedios
Real number (ℝ)

Distinct5330
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean113.39932
Minimum9
Maximum239.28286
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.8 KiB
2023-02-09T01:04:25.764790image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile94.481986
Q1103.77461
median109.60663
Q3116.12285
95-th percentile164.32541
Maximum239.28286
Range230.28286
Interquartile range (IQR)12.348245

Descriptive statistics

Standard deviation21.162057
Coefficient of variation (CV)0.18661538
Kurtosis7.1439966
Mean113.39932
Median Absolute Deviation (MAD)6.0689287
Skewness1.8640114
Sum604645.17
Variance447.83265
MonotonicityNot monotonic
2023-02-09T01:04:25.969015image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48 2
 
< 0.1%
111.8333333 2
 
< 0.1%
197.6174563 1
 
< 0.1%
110.7027653 1
 
< 0.1%
109.2658621 1
 
< 0.1%
109.9539385 1
 
< 0.1%
118.5797672 1
 
< 0.1%
106.3745948 1
 
< 0.1%
109.4493518 1
 
< 0.1%
116.8205128 1
 
< 0.1%
Other values (5320) 5320
99.8%
ValueCountFrequency (%)
9 1
< 0.1%
16 1
< 0.1%
22.07411765 1
< 0.1%
22.32843137 1
< 0.1%
23.66814714 1
< 0.1%
23.99906103 1
< 0.1%
26 1
< 0.1%
26.58750058 1
< 0.1%
27.70181232 1
< 0.1%
27.8346694 1
< 0.1%
ValueCountFrequency (%)
239.2828591 1
< 0.1%
235.2408698 1
< 0.1%
227.6615425 1
< 0.1%
225.7362463 1
< 0.1%
217.8206752 1
< 0.1%
216.6453323 1
< 0.1%
215.8942135 1
< 0.1%
214.8158454 1
< 0.1%
214.6884921 1
< 0.1%
214.2075472 1
< 0.1%

GMD
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct5332
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.81007906
Minimum0.090937372
Maximum1.1958271
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.8 KiB
2023-02-09T01:04:26.080198image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.090937372
5-th percentile0.63834872
Q10.77000405
median0.81925335
Q30.86325692
95-th percentile0.93284536
Maximum1.1958271
Range1.1048898
Interquartile range (IQR)0.093252871

Descriptive statistics

Standard deviation0.088634905
Coefficient of variation (CV)0.10941513
Kurtosis2.7083911
Mean0.81007906
Median Absolute Deviation (MAD)0.046761275
Skewness-0.75243672
Sum4319.3415
Variance0.0078561465
MonotonicityNot monotonic
2023-02-09T01:04:26.181556image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6520848459 1
 
< 0.1%
0.7665510011 1
 
< 0.1%
0.8195818649 1
 
< 0.1%
0.8744983289 1
 
< 0.1%
0.8725804605 1
 
< 0.1%
0.8301805989 1
 
< 0.1%
0.797828938 1
 
< 0.1%
0.7785448373 1
 
< 0.1%
0.8307699353 1
 
< 0.1%
0.7667476886 1
 
< 0.1%
Other values (5322) 5322
99.8%
ValueCountFrequency (%)
0.09093737227 1
< 0.1%
0.2991803279 1
< 0.1%
0.3125 1
< 0.1%
0.3338547052 1
< 0.1%
0.3424015009 1
< 0.1%
0.3766458019 1
< 0.1%
0.386269957 1
< 0.1%
0.4026439483 1
< 0.1%
0.4552194426 1
< 0.1%
0.4627778357 1
< 0.1%
ValueCountFrequency (%)
1.195827141 1
< 0.1%
1.176541043 1
< 0.1%
1.15931215 1
< 0.1%
1.151792385 1
< 0.1%
1.148141848 1
< 0.1%
1.145833333 1
< 0.1%
1.085890399 1
< 0.1%
1.082895861 1
< 0.1%
1.079559371 1
< 0.1%
1.079020042 1
< 0.1%

EntradaInicial
Categorical

Distinct1131
Distinct (%)21.2%
Missing0
Missing (%)0.0%
Memory size41.8 KiB
2018-08-09
 
15
2021-09-13
 
13
2020-01-07
 
12
2021-07-27
 
12
2018-03-26
 
11
Other values (1126)
5269 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters53320
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique92 ?
Unique (%)1.7%

Sample

1st row2018-11-02
2nd row2021-03-13
3rd row2019-09-06
4th row2017-11-03
5th row2020-05-04

Common Values

ValueCountFrequency (%)
2018-08-09 15
 
0.3%
2021-09-13 13
 
0.2%
2020-01-07 12
 
0.2%
2021-07-27 12
 
0.2%
2018-03-26 11
 
0.2%
2018-09-20 11
 
0.2%
2019-10-28 11
 
0.2%
2017-09-19 11
 
0.2%
2021-08-17 11
 
0.2%
2019-09-03 11
 
0.2%
Other values (1121) 5214
97.8%

Length

2023-02-09T01:04:26.295109image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2018-08-09 15
 
0.3%
2021-09-13 13
 
0.2%
2020-01-07 12
 
0.2%
2021-07-27 12
 
0.2%
2021-08-17 11
 
0.2%
2021-06-28 11
 
0.2%
2018-12-04 11
 
0.2%
2019-09-03 11
 
0.2%
2021-06-08 11
 
0.2%
2017-09-19 11
 
0.2%
Other values (1121) 5214
97.8%

Most occurring characters

ValueCountFrequency (%)
0 12974
24.3%
2 11086
20.8%
- 10664
20.0%
1 8838
16.6%
9 2270
 
4.3%
8 2076
 
3.9%
7 1406
 
2.6%
3 1189
 
2.2%
4 959
 
1.8%
5 946
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 42656
80.0%
Dash Punctuation 10664
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12974
30.4%
2 11086
26.0%
1 8838
20.7%
9 2270
 
5.3%
8 2076
 
4.9%
7 1406
 
3.3%
3 1189
 
2.8%
4 959
 
2.2%
5 946
 
2.2%
6 912
 
2.1%
Dash Punctuation
ValueCountFrequency (%)
- 10664
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 53320
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12974
24.3%
2 11086
20.8%
- 10664
20.0%
1 8838
16.6%
9 2270
 
4.3%
8 2076
 
3.9%
7 1406
 
2.6%
3 1189
 
2.2%
4 959
 
1.8%
5 946
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 53320
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12974
24.3%
2 11086
20.8%
- 10664
20.0%
1 8838
16.6%
9 2270
 
4.3%
8 2076
 
3.9%
7 1406
 
2.6%
3 1189
 
2.2%
4 959
 
1.8%
5 946
 
1.8%

EntradaFinal
Categorical

Distinct1198
Distinct (%)22.5%
Missing0
Missing (%)0.0%
Memory size41.8 KiB
2018-10-18
 
14
2020-09-04
 
14
2018-06-08
 
14
2020-04-29
 
12
2020-02-13
 
12
Other values (1193)
5266 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters53320
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique143 ?
Unique (%)2.7%

Sample

1st row2018-12-27
2nd row2021-04-23
3rd row2019-10-03
4th row2017-12-07
5th row2020-06-12

Common Values

ValueCountFrequency (%)
2018-10-18 14
 
0.3%
2020-09-04 14
 
0.3%
2018-06-08 14
 
0.3%
2020-04-29 12
 
0.2%
2020-02-13 12
 
0.2%
2019-07-19 12
 
0.2%
2021-05-19 12
 
0.2%
2020-12-15 11
 
0.2%
2021-11-18 11
 
0.2%
2021-09-09 11
 
0.2%
Other values (1188) 5209
97.7%

Length

2023-02-09T01:04:26.375550image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2018-10-18 14
 
0.3%
2018-06-08 14
 
0.3%
2020-09-04 14
 
0.3%
2020-04-29 12
 
0.2%
2020-02-13 12
 
0.2%
2019-07-19 12
 
0.2%
2021-05-19 12
 
0.2%
2017-09-20 11
 
0.2%
2021-08-12 11
 
0.2%
2019-08-23 11
 
0.2%
Other values (1188) 5209
97.7%

Most occurring characters

ValueCountFrequency (%)
0 12937
24.3%
2 11183
21.0%
- 10664
20.0%
1 8828
16.6%
9 2241
 
4.2%
8 2093
 
3.9%
7 1353
 
2.5%
3 1171
 
2.2%
4 968
 
1.8%
5 942
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 42656
80.0%
Dash Punctuation 10664
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12937
30.3%
2 11183
26.2%
1 8828
20.7%
9 2241
 
5.3%
8 2093
 
4.9%
7 1353
 
3.2%
3 1171
 
2.7%
4 968
 
2.3%
5 942
 
2.2%
6 940
 
2.2%
Dash Punctuation
ValueCountFrequency (%)
- 10664
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 53320
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12937
24.3%
2 11183
21.0%
- 10664
20.0%
1 8828
16.6%
9 2241
 
4.2%
8 2093
 
3.9%
7 1353
 
2.5%
3 1171
 
2.2%
4 968
 
1.8%
5 942
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 53320
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12937
24.3%
2 11183
21.0%
- 10664
20.0%
1 8828
16.6%
9 2241
 
4.2%
8 2093
 
3.9%
7 1353
 
2.5%
3 1171
 
2.2%
4 968
 
1.8%
5 942
 
1.8%

NumAnimales
Real number (ℝ)

Distinct2043
Distinct (%)38.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1728.6277
Minimum24
Maximum8057
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.8 KiB
2023-02-09T01:04:26.471244image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile446
Q11037.5
median1599
Q32108
95-th percentile3348
Maximum8057
Range8033
Interquartile range (IQR)1070.5

Descriptive statistics

Standard deviation1006.8652
Coefficient of variation (CV)0.58246502
Kurtosis5.0960347
Mean1728.6277
Median Absolute Deviation (MAD)549
Skewness1.7276946
Sum9217043
Variance1013777.5
MonotonicityNot monotonic
2023-02-09T01:04:26.577332image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2000 108
 
2.0%
1000 59
 
1.1%
1200 55
 
1.0%
1999 41
 
0.8%
1500 32
 
0.6%
800 30
 
0.6%
384 27
 
0.5%
900 25
 
0.5%
700 22
 
0.4%
1050 19
 
0.4%
Other values (2033) 4914
92.2%
ValueCountFrequency (%)
24 1
 
< 0.1%
69 1
 
< 0.1%
180 1
 
< 0.1%
190 1
 
< 0.1%
193 1
 
< 0.1%
195 3
 
0.1%
196 1
 
< 0.1%
198 2
 
< 0.1%
199 1
 
< 0.1%
200 9
0.2%
ValueCountFrequency (%)
8057 1
< 0.1%
7025 1
< 0.1%
6879 1
< 0.1%
6788 1
< 0.1%
6664 1
< 0.1%
6600 2
< 0.1%
6544 1
< 0.1%
6517 1
< 0.1%
6481 1
< 0.1%
6453 1
< 0.1%

na_nombre
Categorical

Distinct599
Distinct (%)11.2%
Missing0
Missing (%)0.0%
Memory size41.8 KiB
GALVEZ IBERICO
 
65
PICO LA TIENDA
 
65
BUENAVISTA
 
54
RECALCAO
 
40
CORRAL RUBIO
 
30
Other values (594)
5078 

Length

Max length32
Median length27
Mean length9.9771193
Min length3

Characters and Unicode

Total characters53198
Distinct characters48
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique66 ?
Unique (%)1.2%

Sample

1st rowGRANJA - 2
2nd rowGRANJA - 2
3rd rowGRANJA - 2
4th rowGRANJA - 2
5th rowGRANJA - 2

Common Values

ValueCountFrequency (%)
GALVEZ IBERICO 65
 
1.2%
PICO LA TIENDA 65
 
1.2%
BUENAVISTA 54
 
1.0%
RECALCAO 40
 
0.8%
CORRAL RUBIO 30
 
0.6%
FLORIOS ENGORDE 28
 
0.5%
CHOLAS IBERICO 27
 
0.5%
PRADOS 24
 
0.5%
MORTERO 24
 
0.5%
CANONIGO 23
 
0.4%
Other values (589) 4952
92.9%

Length

2023-02-09T01:04:26.690384image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
baja 389
 
4.7%
iberico 221
 
2.7%
cebo 163
 
2.0%
149
 
1.8%
ib 144
 
1.7%
madax 141
 
1.7%
la 102
 
1.2%
1 101
 
1.2%
lo 79
 
1.0%
grande 77
 
0.9%
Other values (541) 6738
81.1%

Most occurring characters

ValueCountFrequency (%)
A 8116
15.3%
O 4540
 
8.5%
E 4077
 
7.7%
R 3979
 
7.5%
I 3314
 
6.2%
L 3059
 
5.8%
2976
 
5.6%
S 2791
 
5.2%
C 2690
 
5.1%
N 2684
 
5.0%
Other values (38) 14972
28.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 47561
89.4%
Space Separator 2976
 
5.6%
Close Punctuation 668
 
1.3%
Open Punctuation 665
 
1.3%
Decimal Number 509
 
1.0%
Other Punctuation 344
 
0.6%
Dash Punctuation 318
 
0.6%
Lowercase Letter 156
 
0.3%
Other Letter 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 8116
17.1%
O 4540
9.5%
E 4077
 
8.6%
R 3979
 
8.4%
I 3314
 
7.0%
L 3059
 
6.4%
S 2791
 
5.9%
C 2690
 
5.7%
N 2684
 
5.6%
B 1561
 
3.3%
Other values (17) 10750
22.6%
Decimal Number
ValueCountFrequency (%)
1 163
32.0%
5 78
15.3%
2 74
14.5%
3 68
13.4%
4 55
 
10.8%
6 25
 
4.9%
7 21
 
4.1%
8 14
 
2.8%
0 6
 
1.2%
9 5
 
1.0%
Lowercase Letter
ValueCountFrequency (%)
a 72
46.2%
j 36
23.1%
b 36
23.1%
ñ 12
 
7.7%
Other Punctuation
ValueCountFrequency (%)
. 253
73.5%
, 91
 
26.5%
Space Separator
ValueCountFrequency (%)
2976
100.0%
Close Punctuation
ValueCountFrequency (%)
) 668
100.0%
Open Punctuation
ValueCountFrequency (%)
( 665
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 318
100.0%
Other Letter
ValueCountFrequency (%)
ª 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 47718
89.7%
Common 5480
 
10.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 8116
17.0%
O 4540
9.5%
E 4077
 
8.5%
R 3979
 
8.3%
I 3314
 
6.9%
L 3059
 
6.4%
S 2791
 
5.8%
C 2690
 
5.6%
N 2684
 
5.6%
B 1561
 
3.3%
Other values (22) 10907
22.9%
Common
ValueCountFrequency (%)
2976
54.3%
) 668
 
12.2%
( 665
 
12.1%
- 318
 
5.8%
. 253
 
4.6%
1 163
 
3.0%
, 91
 
1.7%
5 78
 
1.4%
2 74
 
1.4%
3 68
 
1.2%
Other values (6) 126
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 52881
99.4%
None 317
 
0.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 8116
15.3%
O 4540
 
8.6%
E 4077
 
7.7%
R 3979
 
7.5%
I 3314
 
6.3%
L 3059
 
5.8%
2976
 
5.6%
S 2791
 
5.3%
C 2690
 
5.1%
N 2684
 
5.1%
Other values (34) 14655
27.7%
None
ValueCountFrequency (%)
Ñ 292
92.1%
ñ 12
 
3.8%
Ü 12
 
3.8%
ª 1
 
0.3%

na_rega
Categorical

Distinct469
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Memory size41.8 KiB
ES020370000167
 
76
ES300210640077
 
75
ES020370000205
 
65
ES300243440013
 
54
ES300261140006
 
40
Other values (464)
5022 

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters74648
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)0.4%

Sample

1st rowES300080840002
2nd rowES300080840002
3rd rowES300080840002
4th rowES300080840002
5th rowES300080840002

Common Values

ValueCountFrequency (%)
ES020370000167 76
 
1.4%
ES300210640077 75
 
1.4%
ES020370000205 65
 
1.2%
ES300243440013 54
 
1.0%
ES300261140006 40
 
0.8%
ES300390240018 33
 
0.6%
ES300390740131 30
 
0.6%
ES300210640034 29
 
0.5%
ES300390740055 28
 
0.5%
ES300210340046 27
 
0.5%
Other values (459) 4875
91.4%

Length

2023-02-09T01:04:26.786557image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
es020370000167 76
 
1.4%
es300210640077 75
 
1.4%
es020370000205 65
 
1.2%
es300243440013 54
 
1.0%
es300261140006 40
 
0.8%
es300390240018 33
 
0.6%
es300390740131 30
 
0.6%
es300210640034 29
 
0.5%
es300390740055 28
 
0.5%
es300210340046 27
 
0.5%
Other values (459) 4875
91.4%

Most occurring characters

ValueCountFrequency (%)
0 29436
39.4%
3 7894
 
10.6%
4 6718
 
9.0%
E 5332
 
7.1%
S 5332
 
7.1%
2 4978
 
6.7%
1 4918
 
6.6%
5 2811
 
3.8%
9 1914
 
2.6%
6 1896
 
2.5%
Other values (2) 3419
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 63984
85.7%
Uppercase Letter 10664
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29436
46.0%
3 7894
 
12.3%
4 6718
 
10.5%
2 4978
 
7.8%
1 4918
 
7.7%
5 2811
 
4.4%
9 1914
 
3.0%
6 1896
 
3.0%
7 1876
 
2.9%
8 1543
 
2.4%
Uppercase Letter
ValueCountFrequency (%)
E 5332
50.0%
S 5332
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 63984
85.7%
Latin 10664
 
14.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29436
46.0%
3 7894
 
12.3%
4 6718
 
10.5%
2 4978
 
7.8%
1 4918
 
7.7%
5 2811
 
4.4%
9 1914
 
3.0%
6 1896
 
3.0%
7 1876
 
2.9%
8 1543
 
2.4%
Latin
ValueCountFrequency (%)
E 5332
50.0%
S 5332
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 74648
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29436
39.4%
3 7894
 
10.6%
4 6718
 
9.0%
E 5332
 
7.1%
S 5332
 
7.1%
2 4978
 
6.7%
1 4918
 
6.6%
5 2811
 
3.8%
9 1914
 
2.6%
6 1896
 
2.5%
Other values (2) 3419
 
4.6%

se_nombre
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size41.8 KiB
MACHO ENTERO + HEMBRA
2511 
MACHO ENTERO
1156 
HEMBRA
1104 
MACHO ENTERO + CATRADO+ HEMBRA
524 
MACHO CASTRADO
 
20

Length

Max length30
Median length23
Mean length16.807577
Min length6

Characters and Unicode

Total characters89618
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMACHO ENTERO + CATRADO+ HEMBRA
2nd rowMACHO ENTERO + CATRADO+ HEMBRA
3rd rowMACHO ENTERO + CATRADO+ HEMBRA
4th rowMACHO ENTERO + CATRADO+ HEMBRA
5th rowMACHO ENTERO + CATRADO+ HEMBRA

Common Values

ValueCountFrequency (%)
MACHO ENTERO + HEMBRA 2511
47.1%
MACHO ENTERO 1156
21.7%
HEMBRA 1104
20.7%
MACHO ENTERO + CATRADO+ HEMBRA 524
 
9.8%
MACHO CASTRADO 20
 
0.4%
MACHO CASTRADO + HEMBRA 17
 
0.3%

Length

2023-02-09T01:04:26.867404image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-09T01:04:26.964264image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
macho 4228
26.1%
entero 4191
25.9%
hembra 4156
25.7%
3052
18.9%
catrado 524
 
3.2%
castrado 37
 
0.2%

Most occurring characters

ValueCountFrequency (%)
E 12538
14.0%
10856
12.1%
A 9506
10.6%
O 8980
10.0%
R 8908
9.9%
M 8384
9.4%
H 8384
9.4%
C 4789
 
5.3%
T 4752
 
5.3%
N 4191
 
4.7%
Other values (4) 8330
9.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 75186
83.9%
Space Separator 10856
 
12.1%
Math Symbol 3576
 
4.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 12538
16.7%
A 9506
12.6%
O 8980
11.9%
R 8908
11.8%
M 8384
11.2%
H 8384
11.2%
C 4789
 
6.4%
T 4752
 
6.3%
N 4191
 
5.6%
B 4156
 
5.5%
Other values (2) 598
 
0.8%
Space Separator
ValueCountFrequency (%)
10856
100.0%
Math Symbol
ValueCountFrequency (%)
+ 3576
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 75186
83.9%
Common 14432
 
16.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 12538
16.7%
A 9506
12.6%
O 8980
11.9%
R 8908
11.8%
M 8384
11.2%
H 8384
11.2%
C 4789
 
6.4%
T 4752
 
6.3%
N 4191
 
5.6%
B 4156
 
5.5%
Other values (2) 598
 
0.8%
Common
ValueCountFrequency (%)
10856
75.2%
+ 3576
 
24.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 89618
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 12538
14.0%
10856
12.1%
A 9506
10.6%
O 8980
10.0%
R 8908
9.9%
M 8384
9.4%
H 8384
9.4%
C 4789
 
5.3%
T 4752
 
5.3%
N 4191
 
4.7%
Other values (4) 8330
9.3%

PesoEntMedio
Real number (ℝ)

Distinct5223
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.250188
Minimum6.070862
Maximum145.12205
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.8 KiB
2023-02-09T01:04:27.067050image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum6.070862
5-th percentile18.32
Q122.300142
median25.023817
Q328.55988
95-th percentile45
Maximum145.12205
Range139.05119
Interquartile range (IQR)6.2597379

Descriptive statistics

Standard deviation10.427294
Coefficient of variation (CV)0.38265034
Kurtosis27.133639
Mean27.250188
Median Absolute Deviation (MAD)3.0336466
Skewness4.2129595
Sum145298
Variance108.72845
MonotonicityNot monotonic
2023-02-09T01:04:27.179889image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 15
 
0.3%
35 9
 
0.2%
25 6
 
0.1%
40 5
 
0.1%
23.2 5
 
0.1%
20 5
 
0.1%
24 4
 
0.1%
21.1 3
 
0.1%
45 3
 
0.1%
27.2 3
 
0.1%
Other values (5213) 5274
98.9%
ValueCountFrequency (%)
6.070861978 1
< 0.1%
12.59710586 1
< 0.1%
13.84835479 1
< 0.1%
13.90285714 1
< 0.1%
14.20833333 1
< 0.1%
14.24685714 1
< 0.1%
14.27522936 1
< 0.1%
14.83930211 1
< 0.1%
14.84455959 1
< 0.1%
14.87487487 1
< 0.1%
ValueCountFrequency (%)
145.1220495 1
< 0.1%
142.0851761 1
< 0.1%
140.2492203 1
< 0.1%
140 1
< 0.1%
128.2608696 1
< 0.1%
122 1
< 0.1%
113.4695024 1
< 0.1%
110.6519444 1
< 0.1%
108.8 1
< 0.1%
108.1614235 1
< 0.1%

PesoRecMedio
Real number (ℝ)

Distinct5322
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean118.06948
Minimum26
Maximum200.86943
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.8 KiB
2023-02-09T01:04:27.296470image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile105.39668
Q1111.63569
median115.59942
Q3119.55135
95-th percentile153.90474
Maximum200.86943
Range174.86943
Interquartile range (IQR)7.9156606

Descriptive statistics

Standard deviation14.022412
Coefficient of variation (CV)0.11876406
Kurtosis5.651735
Mean118.06948
Median Absolute Deviation (MAD)3.9600901
Skewness1.0525064
Sum629546.48
Variance196.62802
MonotonicityNot monotonic
2023-02-09T01:04:27.408673image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40 4
 
0.1%
120.3 3
 
0.1%
115.3284672 2
 
< 0.1%
112.5349301 2
 
< 0.1%
109.1954023 2
 
< 0.1%
125.3846154 2
 
< 0.1%
111.1111111 2
 
< 0.1%
101.0589544 1
 
< 0.1%
104.3021077 1
 
< 0.1%
117.6160338 1
 
< 0.1%
Other values (5312) 5312
99.6%
ValueCountFrequency (%)
26 1
 
< 0.1%
35.54399243 1
 
< 0.1%
40 4
0.1%
40.38633819 1
 
< 0.1%
41 1
 
< 0.1%
41.12137203 1
 
< 0.1%
42.56930358 1
 
< 0.1%
43.67021277 1
 
< 0.1%
45.43778802 1
 
< 0.1%
48.2300885 1
 
< 0.1%
ValueCountFrequency (%)
200.8694347 1
< 0.1%
181.7142857 1
< 0.1%
176.7098446 1
< 0.1%
176.3631613 1
< 0.1%
175.6871266 1
< 0.1%
174.250298 1
< 0.1%
173.7755961 1
< 0.1%
172.7649208 1
< 0.1%
170.3801802 1
< 0.1%
169.0909091 1
< 0.1%

NumBajas
Real number (ℝ)

Distinct294
Distinct (%)5.5%
Missing4
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean75.201389
Minimum1
Maximum650
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.8 KiB
2023-02-09T01:04:27.523653image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile14
Q138
median64
Q398
95-th percentile176
Maximum650
Range649
Interquartile range (IQR)60

Descriptive statistics

Standard deviation54.543646
Coefficient of variation (CV)0.72530105
Kurtosis8.7529664
Mean75.201389
Median Absolute Deviation (MAD)29
Skewness2.0654177
Sum400673
Variance2975.0094
MonotonicityNot monotonic
2023-02-09T01:04:27.631222image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
54 61
 
1.1%
42 59
 
1.1%
50 59
 
1.1%
48 58
 
1.1%
53 58
 
1.1%
61 57
 
1.1%
32 56
 
1.1%
27 56
 
1.1%
46 56
 
1.1%
58 56
 
1.1%
Other values (284) 4752
89.1%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 6
 
0.1%
3 5
 
0.1%
4 9
 
0.2%
5 15
0.3%
6 8
 
0.2%
7 20
0.4%
8 15
0.3%
9 29
0.5%
10 35
0.7%
ValueCountFrequency (%)
650 1
< 0.1%
559 1
< 0.1%
548 1
< 0.1%
480 1
< 0.1%
478 1
< 0.1%
425 1
< 0.1%
419 1
< 0.1%
392 1
< 0.1%
376 1
< 0.1%
366 1
< 0.1%

GPS_Longitud
Real number (ℝ)

Distinct456
Distinct (%)8.6%
Missing5
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean-1.6140347
Minimum-4.2884
Maximum-0.48953
Zeros0
Zeros (%)0.0%
Negative5327
Negative (%)99.9%
Memory size41.8 KiB
2023-02-09T01:04:27.839354image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-4.2884
5-th percentile-2.29713
Q1-1.87829
median-1.52367
Q3-1.26675
95-th percentile-1.11354
Maximum-0.48953
Range3.79887
Interquartile range (IQR)0.61154

Descriptive statistics

Standard deviation0.45160514
Coefficient of variation (CV)-0.2797989
Kurtosis4.7891064
Mean-1.6140347
Median Absolute Deviation (MAD)0.28461
Skewness-1.6144185
Sum-8597.963
Variance0.20394721
MonotonicityNot monotonic
2023-02-09T01:04:27.948452image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.52367 141
 
2.6%
-1.17523 75
 
1.4%
-1.48801 65
 
1.2%
-1.90272 54
 
1.0%
-2.94016 44
 
0.8%
-1.29353 40
 
0.8%
-1.4815 33
 
0.6%
-1.47962 30
 
0.6%
-1.18819 29
 
0.5%
-1.19438 29
 
0.5%
Other values (446) 4787
89.8%
ValueCountFrequency (%)
-4.2884 1
 
< 0.1%
-4.26492 3
0.1%
-3.89928 4
0.1%
-3.84777 5
0.1%
-3.7955 4
0.1%
-3.78193 4
0.1%
-3.75126 3
0.1%
-3.73308 4
0.1%
-3.64074 3
0.1%
-3.61298 4
0.1%
ValueCountFrequency (%)
-0.48953 6
 
0.1%
-0.51217 3
 
0.1%
-0.77741 20
0.4%
-0.82821 22
0.4%
-0.87253 4
 
0.1%
-0.93381 11
0.2%
-0.97645 11
0.2%
-0.99753 9
0.2%
-0.99792 9
0.2%
-1.01195 5
 
0.1%

GPS_Latitud
Real number (ℝ)

Distinct453
Distinct (%)8.5%
Missing5
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean37.804895
Minimum37.2525
Maximum40.1577
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.8 KiB
2023-02-09T01:04:28.071911image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum37.2525
5-th percentile37.44628
Q137.6142
median37.71148
Q337.79953
95-th percentile38.87346
Maximum40.1577
Range2.9052
Interquartile range (IQR)0.18533

Descriptive statistics

Standard deviation0.40577266
Coefficient of variation (CV)0.010733337
Kurtosis7.5294328
Mean37.804895
Median Absolute Deviation (MAD)0.08978
Skewness2.6449857
Sum201386.67
Variance0.16465145
MonotonicityNot monotonic
2023-02-09T01:04:28.173357image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38.45461 141
 
2.6%
37.69911 75
 
1.4%
38.42037 65
 
1.2%
37.49124 64
 
1.2%
37.82722 54
 
1.0%
37.6066 40
 
0.8%
37.80726 33
 
0.6%
37.7011 30
 
0.6%
37.64211 29
 
0.5%
37.69884 28
 
0.5%
Other values (443) 4768
89.4%
ValueCountFrequency (%)
37.2525 3
 
0.1%
37.2761 10
0.2%
37.2772 21
0.4%
37.36489 5
 
0.1%
37.3804 14
0.3%
37.38724 10
0.2%
37.38731 6
 
0.1%
37.3953 11
0.2%
37.39601 24
0.5%
37.3979 11
0.2%
ValueCountFrequency (%)
40.1577 3
0.1%
39.72323 1
 
< 0.1%
39.65069 5
0.1%
39.64358 2
 
< 0.1%
39.64133 2
 
< 0.1%
39.63548 4
0.1%
39.58419 5
0.1%
39.58408 4
0.1%
39.58333 5
0.1%
39.58221 4
0.1%

gr_direccion
Categorical

HIGH CARDINALITY  MISSING 

Distinct354
Distinct (%)8.1%
Missing979
Missing (%)18.4%
Memory size41.8 KiB
CANCARIX
 
141
0
 
91
TOTANA
 
69
PARAJE PICO LA TIENDA,CTR MURCIA-MADRID S/N
 
65
MEDIA LEGUA
 
54
Other values (349)
3933 

Length

Max length50
Median length37
Mean length22.737882
Min length1

Characters and Unicode

Total characters98978
Distinct characters45
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)0.3%

Sample

1st rowPARAJE EL PRADO S/N
2nd rowPARAJE EL PRADO S/N
3rd rowPARAJE EL PRADO S/N
4th rowPARAJE EL PRADO S/N
5th rowPARAJE EL PRADO S/N

Common Values

ValueCountFrequency (%)
CANCARIX 141
 
2.6%
0 91
 
1.7%
TOTANA 69
 
1.3%
PARAJE PICO LA TIENDA,CTR MURCIA-MADRID S/N 65
 
1.2%
MEDIA LEGUA 54
 
1.0%
MAZARRON 50
 
0.9%
PARAJE LA PINILLA S/N 49
 
0.9%
CAMPILLO DE ARRIBA 47
 
0.9%
PARAJE LOS PADRES S/N - EL ALBUJON 45
 
0.8%
FINCA PINO GRANDE CTRA EL BAUL S/N 44
 
0.8%
Other values (344) 3698
69.4%
(Missing) 979
 
18.4%

Length

2023-02-09T01:04:28.296439image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
paraje 1711
 
10.1%
s/n 1151
 
6.8%
780
 
4.6%
la 739
 
4.4%
los 611
 
3.6%
el 479
 
2.8%
de 385
 
2.3%
las 301
 
1.8%
media 260
 
1.5%
legua 260
 
1.5%
Other values (508) 10258
60.6%

Most occurring characters

ValueCountFrequency (%)
A 15271
15.4%
12650
12.8%
E 7325
 
7.4%
L 7225
 
7.3%
R 7069
 
7.1%
O 5613
 
5.7%
S 4530
 
4.6%
N 4515
 
4.6%
I 4104
 
4.1%
C 4005
 
4.0%
Other values (35) 26671
26.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 79391
80.2%
Space Separator 12650
 
12.8%
Other Punctuation 2663
 
2.7%
Decimal Number 1956
 
2.0%
Dash Punctuation 1228
 
1.2%
Open Punctuation 422
 
0.4%
Close Punctuation 422
 
0.4%
Other Letter 246
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 15271
19.2%
E 7325
9.2%
L 7225
9.1%
R 7069
8.9%
O 5613
 
7.1%
S 4530
 
5.7%
N 4515
 
5.7%
I 4104
 
5.2%
C 4005
 
5.0%
P 3668
 
4.6%
Other values (16) 16066
20.2%
Decimal Number
ValueCountFrequency (%)
1 457
23.4%
2 393
20.1%
4 251
12.8%
0 171
 
8.7%
5 142
 
7.3%
3 126
 
6.4%
9 122
 
6.2%
6 117
 
6.0%
8 90
 
4.6%
7 87
 
4.4%
Other Punctuation
ValueCountFrequency (%)
/ 1282
48.1%
, 810
30.4%
. 499
 
18.7%
" 72
 
2.7%
Space Separator
ValueCountFrequency (%)
12650
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1228
100.0%
Open Punctuation
ValueCountFrequency (%)
( 422
100.0%
Close Punctuation
ValueCountFrequency (%)
) 422
100.0%
Other Letter
ValueCountFrequency (%)
º 246
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 79637
80.5%
Common 19341
 
19.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 15271
19.2%
E 7325
9.2%
L 7225
9.1%
R 7069
8.9%
O 5613
 
7.0%
S 4530
 
5.7%
N 4515
 
5.7%
I 4104
 
5.2%
C 4005
 
5.0%
P 3668
 
4.6%
Other values (17) 16312
20.5%
Common
ValueCountFrequency (%)
12650
65.4%
/ 1282
 
6.6%
- 1228
 
6.3%
, 810
 
4.2%
. 499
 
2.6%
1 457
 
2.4%
( 422
 
2.2%
) 422
 
2.2%
2 393
 
2.0%
4 251
 
1.3%
Other values (8) 927
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 98375
99.4%
None 603
 
0.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 15271
15.5%
12650
12.9%
E 7325
 
7.4%
L 7225
 
7.3%
R 7069
 
7.2%
O 5613
 
5.7%
S 4530
 
4.6%
N 4515
 
4.6%
I 4104
 
4.2%
C 4005
 
4.1%
Other values (33) 26068
26.5%
None
ValueCountFrequency (%)
Ñ 357
59.2%
º 246
40.8%

gr_codpos
Real number (ℝ)

Distinct90
Distinct (%)1.7%
Missing3
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean23653.413
Minimum2001
Maximum46893
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.8 KiB
2023-02-09T01:04:28.411784image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2001
5-th percentile2499
Q14839
median30320
Q330813
95-th percentile30890
Maximum46893
Range44892
Interquartile range (IQR)25974

Descriptive statistics

Standard deviation12030.826
Coefficient of variation (CV)0.5086296
Kurtosis-0.78245464
Mean23653.413
Median Absolute Deviation (MAD)520
Skewness-0.87851881
Sum1.2604904 × 108
Variance1.4474077 × 108
MonotonicityNot monotonic
2023-02-09T01:04:28.514180image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30320 1060
19.9%
30800 459
 
8.6%
30850 387
 
7.3%
4600 369
 
6.9%
30840 315
 
5.9%
4820 248
 
4.7%
2499 206
 
3.9%
30170 196
 
3.7%
30890 175
 
3.3%
30870 121
 
2.3%
Other values (80) 1793
33.6%
ValueCountFrequency (%)
2001 16
 
0.3%
2002 11
 
0.2%
2130 1
 
< 0.1%
2142 1
 
< 0.1%
2155 20
0.4%
2248 11
 
0.2%
2249 28
0.5%
2250 6
 
0.1%
2260 11
 
0.2%
2328 43
0.8%
ValueCountFrequency (%)
46893 9
 
0.2%
46330 4
 
0.1%
46314 40
0.8%
45710 28
0.5%
45700 3
 
0.1%
45480 15
 
0.3%
45450 9
 
0.2%
45370 3
 
0.1%
45164 1
 
< 0.1%
30891 14
 
0.3%

gr_poblacion
Categorical

Distinct146
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size41.8 KiB
30320-FUENTE ALAMO (MURCIA)
870 
LORCA (MURCIA)
510 
04600-HUERCAL OVERA (ALMERIA)
340 
TOTANA (MURCIA)
336 
ALHAMA DE MURCIA
318 
Other values (141)
2958 

Length

Max length35
Median length31
Mean length19.547262
Min length4

Characters and Unicode

Total characters104226
Distinct characters41
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)0.2%

Sample

1st rowALHAMA DE MURCIA
2nd rowALHAMA DE MURCIA
3rd rowALHAMA DE MURCIA
4th rowALHAMA DE MURCIA
5th rowALHAMA DE MURCIA

Common Values

ValueCountFrequency (%)
30320-FUENTE ALAMO (MURCIA) 870
16.3%
LORCA (MURCIA) 510
 
9.6%
04600-HUERCAL OVERA (ALMERIA) 340
 
6.4%
TOTANA (MURCIA) 336
 
6.3%
ALHAMA DE MURCIA 318
 
6.0%
VELEZ RUBIO (ALMERIA) 202
 
3.8%
FUENTE ALAMO (MURCIA) 179
 
3.4%
MULA (MURCIA) 178
 
3.3%
PUERTO LUMBRERAS (MURCIA) 177
 
3.3%
HELLIN 141
 
2.6%
Other values (136) 2081
39.0%

Length

2023-02-09T01:04:28.632730image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
murcia 2863
20.7%
alamo 1234
 
8.9%
30320-fuente 870
 
6.3%
lorca 776
 
5.6%
almeria 734
 
5.3%
de 503
 
3.6%
446
 
3.2%
overa 445
 
3.2%
totana 375
 
2.7%
fuente 359
 
2.6%
Other values (137) 5203
37.7%

Most occurring characters

ValueCountFrequency (%)
A 15446
14.8%
8476
 
8.1%
R 8032
 
7.7%
E 7377
 
7.1%
L 6787
 
6.5%
M 6011
 
5.8%
U 5775
 
5.5%
C 5416
 
5.2%
I 5072
 
4.9%
O 4499
 
4.3%
Other values (31) 31335
30.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 78782
75.6%
Space Separator 8476
 
8.1%
Decimal Number 7335
 
7.0%
Open Punctuation 3741
 
3.6%
Close Punctuation 3737
 
3.6%
Dash Punctuation 2038
 
2.0%
Other Punctuation 117
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 15446
19.6%
R 8032
10.2%
E 7377
9.4%
L 6787
8.6%
M 6011
 
7.6%
U 5775
 
7.3%
C 5416
 
6.9%
I 5072
 
6.4%
O 4499
 
5.7%
T 3064
 
3.9%
Other values (15) 11303
14.3%
Decimal Number
ValueCountFrequency (%)
0 3184
43.4%
3 1951
26.6%
2 898
 
12.2%
4 490
 
6.7%
6 463
 
6.3%
9 136
 
1.9%
1 103
 
1.4%
8 88
 
1.2%
7 12
 
0.2%
5 10
 
0.1%
Other Punctuation
ValueCountFrequency (%)
, 98
83.8%
. 19
 
16.2%
Space Separator
ValueCountFrequency (%)
8476
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3741
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3737
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2038
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 78782
75.6%
Common 25444
 
24.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 15446
19.6%
R 8032
10.2%
E 7377
9.4%
L 6787
8.6%
M 6011
 
7.6%
U 5775
 
7.3%
C 5416
 
6.9%
I 5072
 
6.4%
O 4499
 
5.7%
T 3064
 
3.9%
Other values (15) 11303
14.3%
Common
ValueCountFrequency (%)
8476
33.3%
( 3741
14.7%
) 3737
14.7%
0 3184
 
12.5%
- 2038
 
8.0%
3 1951
 
7.7%
2 898
 
3.5%
4 490
 
1.9%
6 463
 
1.8%
9 136
 
0.5%
Other values (6) 330
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 104187
> 99.9%
None 39
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 15446
14.8%
8476
 
8.1%
R 8032
 
7.7%
E 7377
 
7.1%
L 6787
 
6.5%
M 6011
 
5.8%
U 5775
 
5.5%
C 5416
 
5.2%
I 5072
 
4.9%
O 4499
 
4.3%
Other values (30) 31296
30.0%
None
ValueCountFrequency (%)
Ñ 39
100.0%

Interactions

2023-02-08T22:23:05.502439image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:19:10.434074image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:20:22.478851image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:23:01.324708image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:28:07.742175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:36:10.840437image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:46:55.263857image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T10:02:04.401076image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T10:21:43.481980image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T10:46:57.882321image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T11:18:40.133032image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T11:57:12.382737image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T12:43:18.808750image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T15:26:42.095953image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T16:50:27.168119image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-08T07:02:28.503190image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-08T20:42:08.982219image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-08T22:29:02.980675image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:19:12.682330image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:20:28.728351image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:23:14.110996image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:28:32.148742image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:36:43.401714image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:47:38.591389image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T10:03:07.649261image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T10:23:01.733880image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T10:48:39.508994image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T11:20:44.521893image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T11:59:42.859644image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T12:46:26.253998image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T15:30:12.983498image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T16:56:48.043019image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-08T07:30:22.910324image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-08T20:47:21.821289image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-08T22:35:02.745719image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:19:14.962997image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:20:35.492250image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:23:27.043187image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:28:57.274823image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:37:16.113607image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:48:25.639114image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T10:04:13.899512image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T10:24:21.189793image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T10:50:21.639896image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T11:22:49.736298image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T12:02:13.896921image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T12:49:33.848762image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T15:33:41.801399image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T22:09:25.873868image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-08T13:47:26.478572image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-08T20:52:36.956523image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-08T22:41:06.370666image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:19:17.337992image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:20:42.539356image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:23:41.025611image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:29:22.886198image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:37:50.036791image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:49:14.140279image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T10:05:21.386667image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T10:25:41.480849image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T10:52:05.603714image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T11:24:57.217030image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T12:04:46.276454image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T13:05:33.906851image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T15:37:28.217201image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T22:13:38.180938image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-08T13:56:40.124918image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-08T20:57:55.951171image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-08T22:47:11.115710image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:19:19.904492image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:20:49.775908image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:23:55.807522image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:29:48.675705image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:38:25.303751image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:50:03.757020image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T10:06:24.094035image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T10:27:03.055663image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T10:53:52.145634image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T11:27:05.916238image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T12:07:19.906126image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T13:08:34.980645image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T15:41:13.691129image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T22:17:43.287856image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-08T14:10:32.257562image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-08T21:03:16.867985image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-08T22:53:17.943686image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:19:22.956002image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:20:57.912248image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:24:11.202731image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:30:15.817513image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:39:00.708988image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:50:55.118377image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T10:07:27.928795image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T10:28:26.229457image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T10:55:38.455685image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T11:29:17.755018image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T12:09:56.252222image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T13:11:36.686105image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T15:44:59.884920image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T22:21:53.048194image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-08T19:42:11.698853image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-08T21:08:39.953875image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-08T22:59:27.485070image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:19:26.553760image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:21:06.162904image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:24:27.012067image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:30:42.943497image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:39:36.611696image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:51:48.646124image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T10:08:32.033151image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T10:29:50.468183image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T10:57:26.903362image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T11:31:29.654194image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T12:12:33.626764image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T13:14:40.103674image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T15:48:44.878737image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T22:26:01.068569image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-08T19:46:58.932958image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-08T21:19:39.477248image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-08T23:05:38.418171image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:19:30.384923image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:21:14.931352image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:24:45.022743image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:31:11.359427image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:40:13.680430image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:52:41.768482image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T10:09:41.205134image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T10:31:16.092129image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T10:59:16.262304image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T11:33:43.611605image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T12:15:12.716070image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T13:17:50.033572image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T15:52:28.696422image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T22:30:15.675198image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-08T19:51:48.795602image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-08T21:25:08.079752image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-08T23:11:51.472219image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:19:34.305422image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:21:24.234162image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:25:06.405243image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:31:39.744424image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:40:50.851402image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:53:36.094620image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T10:10:48.962875image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T10:32:46.709542image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T11:01:07.297971image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T11:35:57.454401image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T12:17:52.613561image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T13:21:05.015137image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T15:56:10.454011image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T22:34:29.664180image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-08T19:56:38.926487image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-08T21:30:37.789657image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-08T23:18:07.251865image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:19:38.863504image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:21:33.718168image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:25:25.741771image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:32:08.387877image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:41:28.986750image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:54:31.733503image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T10:11:57.232585image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T10:34:15.673103image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T11:02:59.961328image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T11:38:12.784751image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T12:20:34.050189image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T13:25:42.033989image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T15:59:52.922300image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T22:38:44.835179image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-08T20:01:31.238419image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-08T21:36:09.246338image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-08T23:24:25.300090image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:19:43.229297image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:21:43.496471image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:25:42.016522image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:32:37.631612image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:42:07.345161image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:55:26.133615image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T10:13:06.595959image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T10:35:47.788490image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T11:04:52.440075image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T11:40:29.432821image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T12:23:18.512210image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T13:28:52.146175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T16:21:22.841164image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T22:43:01.912972image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-08T20:06:30.641962image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-08T21:41:44.732190image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-08T23:30:47.419047image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:19:47.958599image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:21:53.168481image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:26:00.385312image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:33:07.164828image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:42:46.882176image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:56:19.123459image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T10:14:17.308247image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T10:37:19.123400image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T11:06:46.554956image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T11:42:47.718762image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T12:26:06.133500image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T13:32:04.380521image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T16:25:26.555907image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T22:47:22.557385image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-08T20:11:31.374436image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-08T21:47:26.279956image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-08T23:37:12.052548image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:19:52.858893image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:22:03.376435image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:26:20.382613image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:33:37.220259image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:43:26.706548image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:57:13.287256image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T10:15:28.931123image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T10:38:51.578629image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T11:08:42.392926image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T11:45:07.163189image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T12:28:55.356646image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T13:35:17.960029image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T16:29:32.402382image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T22:51:43.972961image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-08T20:16:30.171066image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-08T21:53:07.771112image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-08T23:43:40.540644image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:19:58.135962image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:22:13.483410image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:26:40.410988image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:34:05.338225image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:44:07.417394image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:58:07.807974image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T10:16:41.094870image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T10:40:25.559719image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T11:10:39.293472image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T11:47:28.582633image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T12:31:44.023390image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T13:38:38.540938image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T16:33:41.528697image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T22:56:07.702278image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-08T20:21:32.261389image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-08T21:58:53.600851image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-08T23:50:09.800314image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:20:03.619527image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:22:24.323243image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:27:00.763575image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:34:37.378013image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:44:48.648401image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:59:04.377846image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T10:17:54.698890image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T10:42:01.010210image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T11:12:36.862777image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T11:49:52.475262image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T12:34:32.389585image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T13:42:03.193223image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T16:37:50.672575image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T23:00:47.000324image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-08T20:26:37.388443image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-08T22:04:41.496606image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-08T23:56:42.281914image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:20:09.089864image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:22:36.303247image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:27:21.649876image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:35:07.999687image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:45:30.205215image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T10:00:03.481380image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T10:19:09.851595image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T10:43:38.105278image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T11:14:37.335325image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T11:52:17.259415image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T12:37:24.371893image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T13:45:28.415605image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T16:42:02.132199image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T23:05:34.815331image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-08T20:31:45.925381image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-08T22:10:32.278656image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-09T00:03:17.860398image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:20:16.469552image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:22:48.626503image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:27:44.405597image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:35:39.354981image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T09:46:12.238989image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T10:01:02.938654image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T10:20:26.083405image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T10:45:17.455374image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T11:16:38.057578image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T11:54:44.021847image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T12:40:16.419476image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T15:23:09.308489image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T16:46:13.702089image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-08T06:48:47.085771image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-08T20:36:55.322964image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-08T22:17:06.738320image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-02-09T01:04:28.739774image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ct_codigoct_integract_granjact_navect_razact_fasect_sexoIncPesoDiasMediosGMDNumAnimalesPesoEntMedioPesoRecMedioNumBajasGPS_LongitudGPS_Latitudgr_codposct_tipoct_tipo_alise_nombre
ct_codigo1.0000.280-0.131-0.1460.5620.111-0.0650.3310.0660.287-0.022-0.2470.2490.060-0.011-0.0350.0860.3850.3870.175
ct_integra0.2801.000-0.181-0.2660.0850.073-0.181-0.004-0.1140.219-0.054-0.177-0.066-0.011-0.076-0.151-0.0220.4270.4260.206
ct_granja-0.131-0.1811.0000.1660.093-0.2830.0160.0770.193-0.268-0.1230.1330.091-0.0100.103-0.010-0.0640.5850.5840.268
ct_nave-0.146-0.2660.1661.0000.0290.1170.4310.0540.176-0.2850.1190.3310.1990.1000.1510.156-0.1340.4620.4630.203
ct_raza0.5620.0850.0930.0291.000-0.1030.0540.2750.179-0.024-0.065-0.0270.2660.0700.0420.0950.0260.9410.9370.438
ct_fase0.1110.073-0.2830.117-0.1031.0000.637-0.133-0.1540.1100.2430.040-0.0630.1260.0010.039-0.0530.2490.2490.989
ct_sexo-0.065-0.1810.0160.4310.0540.6371.0000.0900.168-0.2890.1380.3830.2760.0410.0390.140-0.1141.0000.9961.000
IncPeso0.331-0.0040.0770.0540.275-0.1330.0901.0000.5190.214-0.101-0.2620.735-0.0380.0010.006-0.0430.6910.6880.310
DiasMedios0.066-0.1140.1930.1760.179-0.1540.1680.5191.000-0.607-0.147-0.2660.2410.0830.086-0.0210.0050.8280.8250.383
GMD0.2870.219-0.268-0.285-0.0240.110-0.2890.214-0.6071.0000.068-0.1380.261-0.095-0.109-0.0340.0120.7660.7650.357
NumAnimales-0.022-0.054-0.1230.119-0.0650.2430.138-0.101-0.1470.0681.0000.089-0.0380.7690.0800.087-0.0490.5520.5530.271
PesoEntMedio-0.247-0.1770.1330.331-0.0270.0400.383-0.262-0.266-0.1380.0891.0000.322-0.0240.0510.176-0.0740.7600.7590.363
PesoRecMedio0.249-0.0660.0910.1990.266-0.0630.2760.7350.2410.261-0.0380.3221.000-0.0690.0120.096-0.0710.9320.9280.420
NumBajas0.060-0.011-0.0100.1000.0700.1260.041-0.0380.083-0.0950.769-0.024-0.0691.0000.1830.0690.0240.2030.2030.100
GPS_Longitud-0.011-0.0760.1030.1510.0420.0010.0390.0010.086-0.1090.0800.0510.0120.1831.0000.2960.2040.2410.2410.152
GPS_Latitud-0.035-0.151-0.0100.1560.0950.0390.1400.006-0.021-0.0340.0870.1760.0960.0690.2961.0000.0610.3970.3970.195
gr_codpos0.086-0.022-0.064-0.1340.026-0.053-0.114-0.0430.0050.012-0.049-0.074-0.0710.0240.2040.0611.0000.0960.0970.074
ct_tipo0.3850.4270.5850.4620.9410.2491.0000.6910.8280.7660.5520.7600.9320.2030.2410.3970.0961.0000.9961.000
ct_tipo_ali0.3870.4260.5840.4630.9370.2490.9960.6880.8250.7650.5530.7590.9280.2030.2410.3970.0970.9961.0000.996
se_nombre0.1750.2060.2680.2030.4380.9891.0000.3100.3830.3570.2710.3630.4200.1000.1520.1950.0741.0000.9961.000

Missing values

2023-02-09T00:09:55.958831image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-09T00:16:36.462203image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-02-09T00:23:19.274558image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

ct_codigoct_integract_granjact_navect_tipoct_razact_fasect_sexoct_ali_liquidact_tipo_aliIncPesoDiasMediosGMDEntradaInicialEntradaFinalNumAnimalesna_nombrena_regase_nombrePesoEntMedioPesoRecMedioNumBajasGPS_LongitudGPS_Latitudgr_direcciongr_codposgr_poblacion
020312124026928N2128.863349197.6174560.6520852018-11-022018-12-276174GRANJA - 2ES300080840002MACHO ENTERO + CATRADO+ HEMBRA36.710075165.573423229.0-1.3940337.84721PARAJE EL PRADO S/N30840.0ALHAMA DE MURCIA
120315124026928N2112.100242180.3653550.6215182021-03-132021-04-235557GRANJA - 2ES300080840002MACHO ENTERO + CATRADO+ HEMBRA41.079359153.179601145.0-1.3940337.84721PARAJE EL PRADO S/N30840.0ALHAMA DE MURCIA
220313124026928N2119.896276191.9902420.6244922019-09-062019-10-036174GRANJA - 2ES300080840002MACHO ENTERO + CATRADO+ HEMBRA36.938452156.834727196.0-1.3940337.84721PARAJE EL PRADO S/N30840.0ALHAMA DE MURCIA
320311124026928N2115.469246196.8999400.5864362017-11-032017-12-074116GRANJA - 2ES300080840002MACHO ENTERO + CATRADO+ HEMBRA40.997813156.467060215.0-1.3940337.84721PARAJE EL PRADO S/N30840.0ALHAMA DE MURCIA
420314124026928N2115.154338184.7929450.6231532020-05-042020-06-126174GRANJA - 2ES300080840002MACHO ENTERO + CATRADO+ HEMBRA39.544056154.698394196.0-1.3940337.84721PARAJE EL PRADO S/N30840.0ALHAMA DE MURCIA
5203161240269208N229.72463846.2898550.6421422021-04-062021-04-0669GRANJA - 2ES300080840002MACHO ENTERO + CATRADO+ HEMBRA128.260870157.985507NaN-1.3940337.84721PARAJE EL PRADO S/N30840.0ALHAMA DE MURCIA
62136512031022N185.823902123.9710430.6922902019-04-262019-09-23394PICO LA TIENDAES020370000205MACHO ENTERO + HEMBRA37.817259123.64116115.0-1.4880138.42037PARAJE PICO LA TIENDA,CTR MURCIA-MADRID S/N2499.0CANCARIX, HELLIN (ALBACETE)
72176012071022N199.329224154.3915540.6433592019-05-292019-10-09416PICO LA TIENDAES020370000205MACHO ENTERO + HEMBRA30.721154130.05037819.0-1.4880138.42037PARAJE PICO LA TIENDA,CTR MURCIA-MADRID S/N2499.0CANCARIX, HELLIN (ALBACETE)
82196312091022N1120.625000164.7468750.7321842018-06-222018-06-22342PICO LA TIENDAES020370000205MACHO ENTERO + HEMBRA30.000000150.62500022.0-1.4880138.42037PARAJE PICO LA TIENDA,CTR MURCIA-MADRID S/N2499.0CANCARIX, HELLIN (ALBACETE)
92224912021022N1109.638124155.3730030.7056452019-05-152019-05-22394PICO LA TIENDAES020370000205MACHO ENTERO + HEMBRA30.177665139.81578914.0-1.4880138.42037PARAJE PICO LA TIENDA,CTR MURCIA-MADRID S/N2499.0CANCARIX, HELLIN (ALBACETE)
ct_codigoct_integract_granjact_navect_tipoct_razact_fasect_sexoct_ali_liquidact_tipo_aliIncPesoDiasMediosGMDEntradaInicialEntradaFinalNumAnimalesna_nombrena_regase_nombrePesoEntMedioPesoRecMedioNumBajasGPS_LongitudGPS_Latitudgr_direcciongr_codposgr_poblacion
53222052736401119422N196.705419102.2919730.9453862021-08-202021-08-262582MIÑANOES300290540008MACHO ENTERO + HEMBRA25.236251121.94167079.0-1.6186837.91622PARAJE PRAO MIÑANO S/N30177.0CASAS NUEVAS (MULA)
53232055396417118422N194.877570110.9283980.8553052021-10-112021-10-181586RAMBLETAES300160340040MACHO ENTERO + HEMBRA25.056747119.93431694.0-1.0599937.67478PARAJE LOS HERNANDEZ S/N30390.030390-LA ALJORRA, CARTAGENA
532420558664211184214N192.989526103.2071670.9009992021-10-202021-10-221719MACARIOES020310000050MACHO ENTERO28.353694121.34322067.0-1.9763238.36820PARAJE LA CABAÑUELA - PREC.224 POL.282436.0FEREZ - ALBACETE
532520564064311194214N197.262158113.9623870.8534582021-10-272021-10-281130CATYES040530000231MACHO ENTERO20.318584117.580742133.0-1.9114637.44760PARAJE DE URCAL Nº194600.0HUERCAL OVERA
532620574264411184225N197.252139114.7062200.8478372021-11-172021-11-181990CANALONES020010000023HEMBRA26.281407123.533546112.0-1.4975339.22613PARAJE CANALON POLIG. 8 PARCELA 1252130.0ABENGIBRE
532720586564512193225N189.65310486.7254381.0337582021-12-172021-12-201760PRADOSES300243540001HEMBRA32.274432121.92753635.0-1.7031737.87150NaN30800.0LORCA (MURCIA)
532820586464511193214N193.62392586.7242021.0795592021-12-132021-12-171920PRADOSES300243540001MACHO ENTERO30.524479124.14840440.0-1.7031737.87150NaN30800.0LORCA (MURCIA)
532920582564611184214N191.368970101.5520880.8997252021-12-072021-12-10900HIGUERAES450670000253MACHO ENTERO32.088889123.45785922.0-4.2884039.72323CMO.DE LOS CASTILLOS, CUARTO DE LA DEHESILLA45164.0GALVEZ
53302056256478118422N190.636188101.2545970.8951322021-10-292021-11-031990CHORLITOSES300210340054MACHO ENTERO + HEMBRA30.874372121.51056096.0-1.2814437.73186NaN30338.0FUENTE ALAMO (MURCIA)
53312059476493118422N188.114371105.3714960.8362262021-12-312022-01-051763VIÑA VIEJAES300210740042MACHO ENTERO + HEMBRA26.859898114.97426953.0-1.2835937.69240LOMA DE LAS BURRAS S/N30335.0LA PINILLA - FUENTE ALAMO